人脸生成(Face Generation)

在该项目中,你将使用生成式对抗网络(Generative Adversarial Nets)来生成新的人脸图像。

获取数据

该项目将使用以下数据集:

  • MNIST
  • CelebA

由于 CelebA 数据集比较复杂,而且这是你第一次使用 GANs。我们想让你先在 MNIST 数据集上测试你的 GANs 模型,以让你更快的评估所建立模型的性能。

如果你在使用 FloydHub, 请将 data_dir 设置为 "/input" 并使用 FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

探索数据(Explore the Data)

MNIST

MNIST 是一个手写数字的图像数据集。你可以更改 show_n_images 探索此数据集。

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0xd0e9c99898>

CelebA

CelebFaces Attributes Dataset (CelebA) 是一个包含 20 多万张名人图片及相关图片说明的数据集。你将用此数据集生成人脸,不会用不到相关说明。你可以更改 show_n_images 探索此数据集。

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0xd0e9d3e9e8>

预处理数据(Preprocess the Data)

由于该项目的重点是建立 GANs 模型,我们将为你预处理数据。

经过数据预处理,MNIST 和 CelebA 数据集的值在 28×28 维度图像的 [-0.5, 0.5] 范围内。CelebA 数据集中的图像裁剪了非脸部的图像部分,然后调整到 28x28 维度。

MNIST 数据集中的图像是单通道的黑白图像,CelebA 数据集中的图像是 三通道的 RGB 彩色图像

建立神经网络(Build the Neural Network)

你将通过部署以下函数来建立 GANs 的主要组成部分:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

检查 TensorFlow 版本并获取 GPU 型号

检查你是否使用正确的 TensorFlow 版本,并获取 GPU 型号

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

输入(Input)

部署 model_inputs 函数以创建用于神经网络的 占位符 (TF Placeholders)。请创建以下占位符:

  • 输入图像占位符: 使用 image_widthimage_heightimage_channels 设置为 rank 4。
  • 输入 Z 占位符: 设置为 rank 2,并命名为 z_dim
  • 学习速率占位符: 设置为 rank 0。

返回占位符元组的形状为 (tensor of real input images, tensor of z data, learning rate)。

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_image = tf.placeholder(dtype=tf.float32, shape=[None, image_width, image_height, image_channels])
    z_dim =  tf.placeholder(dtype=tf.float32, shape=[None,z_dim], name="z_dim")
    learning_rate = tf.placeholder(tf.float32, name="learning_rate")
    
    return input_image, z_dim, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

辨别器(Discriminator)

部署 discriminator 函数创建辨别器神经网络以辨别 images。该函数应能够重复使用神经网络中的各种变量。 在 tf.variable_scope 中使用 "discriminator" 的变量空间名来重复使用该函数中的变量。

该函数应返回形如 (tensor output of the discriminator, tensor logits of the discriminator) 的元组。

In [58]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = 0.2
    with tf.variable_scope('discriminator', reuse=reuse):
        #input: 28x28x3
        
        layer1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', 
                                  kernel_initializer=tf.contrib.layers.xavier_initializer())
        relu1 = tf.maximum(alpha * layer1, layer1)
        drop1 = tf.nn.dropout(relu1, keep_prob=0.8)
        #14*14*32
        
        layer2 = tf.layers.conv2d(drop1, 128, 5, strides=2, padding='same',
                                 kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn2 = tf.layers.batch_normalization(layer2, trainable=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        drop2 = tf.nn.dropout(relu2, keep_prob=0.8)
        #7*7*64
        
#         layer3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
#         bn3 = tf.layers.batch_normalization(layer3, trainable=True)
#         relu3 = tf.maximum(alpha * bn3, bn3)
#         drop3 = tf.nn.dropout(relu3, keep_prob=0.8)
        #3x3x128
        
        flat= tf.reshape(drop2, (-1, 7*7*128))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)     
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

生成器(Generator)

部署 generator 函数以使用 z 生成图像。该函数应能够重复使用神经网络中的各种变量。 在 tf.variable_scope 中使用 "generator" 的变量空间名来重复使用该函数中的变量。

该函数应返回所生成的 28 x 28 x out_channel_dim 维度图像。

In [65]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha = 0.2
    with tf.variable_scope('generator', reuse=not is_train):
        layer1 = tf.layers.dense(z, 4*4*512)
        
        layer1 = tf.reshape(layer1, (-1, 4, 4, 512))
        layer1 = tf.layers.batch_normalization(layer1, training=is_train)
        layer1 = tf.maximum(alpha * layer1, layer1)
        # 4x4x512
        
        layer2 = tf.layers.conv2d_transpose(layer1, 256, 5, strides=2, padding='same')
        layer2 = tf.layers.batch_normalization(layer2, training=is_train)
        layer2 = tf.maximum(alpha * layer2, layer2)
        # 8*8*256
        
        layer3 = tf.layers.conv2d_transpose(layer2, 128, 5, strides=2, padding='same')
        layer3 = tf.layers.batch_normalization(layer3, training=is_train)
        layer3 = tf.maximum(alpha * layer3, layer3)
        #16*16*128
        
        logits = tf.layers.conv2d_transpose(layer3, out_channel_dim, 5, strides=2, padding='same')
        #logits = tf.layers.batch_normalization(logits, training=is_train)
        logits = tf.image.resize_images(images=logits, size=(28,28))

        # 28x28xout
        
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

损失函数(Loss)

部署 model_loss 函数训练并计算 GANs 的损失。该函数应返回形如 (discriminator loss, generator loss) 的元组。

使用你已实现的函数:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [66]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_real_out, d_real_logits = discriminator(images=input_real)
    d_fake_out, d_fake_logits = discriminator(g_model, reuse=True)
    
    smooth = 0.1
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_real_logits,
        labels=tf.ones_like(d_real_out)*(1-smooth)))
    
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_fake_logits, 
        labels = tf.zeros_like(d_fake_out)))
    
    d_loss = d_loss_real+d_loss_fake
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
    logits = d_fake_logits, labels = tf.ones_like(d_fake_out)))    
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

优化(Optimization)

部署 model_opt 函数实现对 GANs 的优化。使用 tf.trainable_variables 获取可训练的所有变量。通过变量空间名 discriminatorgenerator 来过滤变量。该函数应返回形如 (discriminator training operation, generator training operation) 的元组。

In [67]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

训练神经网络(Neural Network Training)

输出显示

使用该函数可以显示生成器 (Generator) 在训练过程中的当前输出,这会帮你评估 GANs 模型的训练程度。

In [68]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

训练

部署 train 函数以建立并训练 GANs 模型。记得使用以下你已完成的函数:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

使用 show_generator_output 函数显示 generator 在训练过程中的输出。

注意:在每个批次 (batch) 中运行 show_generator_output 函数会显著增加训练时间与该 notebook 的体积。推荐每 100 批次输出一次 generator 的输出。

In [69]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    #tf.reset_default_graph()
    
    input_real, input_z, learning_rate_tensor=model_inputs(image_height=data_shape[1],
                                               image_width=data_shape[2],
                                               image_channels=data_shape[3],
                                               z_dim = z_dim)
    
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate_tensor, beta1)
    
    step = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                step += 1
                # sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run optimizers
                batch_images = batch_images*2
                _ = sess.run(d_opt, feed_dict={input_real:batch_images,
                                               input_z: batch_z, 
                                               learning_rate_tensor: learning_rate})
                                
                _ = sess.run(g_opt, feed_dict={input_z: batch_z,
                                              learning_rate_tensor: learning_rate})
                
                if step % 100 == 0:
                    train_loss_d = d_loss.eval({input_z:batch_z, input_real:batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                    show_generator_output(sess, batch_size, input_z, data_shape[3], data_image_mode)
                
                
                

MNIST

在 MNIST 上测试你的 GANs 模型。经过 2 次迭代,GANs 应该能够生成类似手写数字的图像。确保生成器 (generator) 低于辨别器 (discriminator) 的损失,或接近 0。

In [70]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.3808... Generator Loss: 0.7900
Epoch 1/2... Discriminator Loss: 1.4065... Generator Loss: 0.8043
Epoch 1/2... Discriminator Loss: 1.3920... Generator Loss: 0.7749
Epoch 1/2... Discriminator Loss: 1.3894... Generator Loss: 0.7726
Epoch 1/2... Discriminator Loss: 1.3965... Generator Loss: 0.8355
Epoch 1/2... Discriminator Loss: 1.3159... Generator Loss: 0.8445
Epoch 1/2... Discriminator Loss: 1.2383... Generator Loss: 1.0347
Epoch 1/2... Discriminator Loss: 1.4006... Generator Loss: 0.7891
Epoch 1/2... Discriminator Loss: 1.3113... Generator Loss: 0.8830
Epoch 2/2... Discriminator Loss: 1.4506... Generator Loss: 0.7777
Epoch 2/2... Discriminator Loss: 1.3102... Generator Loss: 0.9890
Epoch 2/2... Discriminator Loss: 1.3496... Generator Loss: 0.7667
Epoch 2/2... Discriminator Loss: 1.3652... Generator Loss: 0.8538
Epoch 2/2... Discriminator Loss: 1.4085... Generator Loss: 0.7594
Epoch 2/2... Discriminator Loss: 1.4020... Generator Loss: 0.7399
Epoch 2/2... Discriminator Loss: 1.3737... Generator Loss: 0.8362
Epoch 2/2... Discriminator Loss: 1.3820... Generator Loss: 0.7278
Epoch 2/2... Discriminator Loss: 1.3525... Generator Loss: 0.7746

CelebA

在 CelebA 上运行你的 GANs 模型。在一般的GPU上运行每次迭代大约需要 20 分钟。你可以运行整个迭代,或者当 GANs 开始产生真实人脸图像时停止它。

In [71]:
batch_size = 16
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 1.3001... Generator Loss: 0.7586
Epoch 1/1... Discriminator Loss: 1.4603... Generator Loss: 0.7931
Epoch 1/1... Discriminator Loss: 1.2360... Generator Loss: 0.9584
Epoch 1/1... Discriminator Loss: 1.4427... Generator Loss: 0.7522
Epoch 1/1... Discriminator Loss: 1.4845... Generator Loss: 0.6882
Epoch 1/1... Discriminator Loss: 1.3215... Generator Loss: 0.9547
Epoch 1/1... Discriminator Loss: 1.3497... Generator Loss: 1.0809
Epoch 1/1... Discriminator Loss: 1.5838... Generator Loss: 0.7804
Epoch 1/1... Discriminator Loss: 1.3002... Generator Loss: 0.8994
Epoch 1/1... Discriminator Loss: 1.3197... Generator Loss: 0.8457
Epoch 1/1... Discriminator Loss: 1.4597... Generator Loss: 0.8889
Epoch 1/1... Discriminator Loss: 1.2379... Generator Loss: 0.8065
Epoch 1/1... Discriminator Loss: 1.4717... Generator Loss: 0.8515
Epoch 1/1... Discriminator Loss: 1.3173... Generator Loss: 0.8424
Epoch 1/1... Discriminator Loss: 1.3927... Generator Loss: 0.7111
Epoch 1/1... Discriminator Loss: 1.4433... Generator Loss: 0.6880
Epoch 1/1... Discriminator Loss: 1.6755... Generator Loss: 0.5620
Epoch 1/1... Discriminator Loss: 1.3385... Generator Loss: 0.7927
Epoch 1/1... Discriminator Loss: 1.4088... Generator Loss: 0.8122
Epoch 1/1... Discriminator Loss: 1.2501... Generator Loss: 0.8421
Epoch 1/1... Discriminator Loss: 1.3119... Generator Loss: 0.8760
Epoch 1/1... Discriminator Loss: 1.5330... Generator Loss: 0.7411
Epoch 1/1... Discriminator Loss: 1.2956... Generator Loss: 0.8209
Epoch 1/1... Discriminator Loss: 1.3096... Generator Loss: 0.7762
Epoch 1/1... Discriminator Loss: 1.2277... Generator Loss: 0.7955
Epoch 1/1... Discriminator Loss: 1.4200... Generator Loss: 0.9071
Epoch 1/1... Discriminator Loss: 1.3758... Generator Loss: 0.7914
Epoch 1/1... Discriminator Loss: 1.3438... Generator Loss: 0.8040
Epoch 1/1... Discriminator Loss: 1.4394... Generator Loss: 0.7737
Epoch 1/1... Discriminator Loss: 1.4638... Generator Loss: 0.7539
Epoch 1/1... Discriminator Loss: 1.3930... Generator Loss: 0.8200
Epoch 1/1... Discriminator Loss: 1.3773... Generator Loss: 0.7417
Epoch 1/1... Discriminator Loss: 1.2849... Generator Loss: 0.7815
Epoch 1/1... Discriminator Loss: 1.3424... Generator Loss: 0.8736
Epoch 1/1... Discriminator Loss: 1.4276... Generator Loss: 0.8493
Epoch 1/1... Discriminator Loss: 1.2417... Generator Loss: 0.8135
Epoch 1/1... Discriminator Loss: 1.3777... Generator Loss: 0.7264
Epoch 1/1... Discriminator Loss: 1.4355... Generator Loss: 0.8119
Epoch 1/1... Discriminator Loss: 1.3843... Generator Loss: 0.7534
Epoch 1/1... Discriminator Loss: 1.2967... Generator Loss: 0.8819
Epoch 1/1... Discriminator Loss: 1.2980... Generator Loss: 0.7573
Epoch 1/1... Discriminator Loss: 1.2311... Generator Loss: 0.8828
Epoch 1/1... Discriminator Loss: 1.3755... Generator Loss: 0.8599
Epoch 1/1... Discriminator Loss: 1.3509... Generator Loss: 0.7319
Epoch 1/1... Discriminator Loss: 1.4442... Generator Loss: 0.7500
Epoch 1/1... Discriminator Loss: 1.3503... Generator Loss: 0.7542
Epoch 1/1... Discriminator Loss: 1.3939... Generator Loss: 0.7833
Epoch 1/1... Discriminator Loss: 1.3202... Generator Loss: 0.7212
Epoch 1/1... Discriminator Loss: 1.2581... Generator Loss: 0.8676
Epoch 1/1... Discriminator Loss: 1.3235... Generator Loss: 0.7778
Epoch 1/1... Discriminator Loss: 1.4115... Generator Loss: 0.7093
Epoch 1/1... Discriminator Loss: 1.4554... Generator Loss: 0.7391
Epoch 1/1... Discriminator Loss: 1.3154... Generator Loss: 0.8980
Epoch 1/1... Discriminator Loss: 1.4736... Generator Loss: 0.8635
Epoch 1/1... Discriminator Loss: 1.3497... Generator Loss: 0.8730
Epoch 1/1... Discriminator Loss: 1.4975... Generator Loss: 0.7520
Epoch 1/1... Discriminator Loss: 1.4435... Generator Loss: 0.7532
Epoch 1/1... Discriminator Loss: 1.4050... Generator Loss: 0.6749
Epoch 1/1... Discriminator Loss: 1.4312... Generator Loss: 0.7195
Epoch 1/1... Discriminator Loss: 1.3606... Generator Loss: 0.8624
Epoch 1/1... Discriminator Loss: 1.3273... Generator Loss: 0.8839
Epoch 1/1... Discriminator Loss: 1.3621... Generator Loss: 0.8733
Epoch 1/1... Discriminator Loss: 1.3902... Generator Loss: 0.7941
Epoch 1/1... Discriminator Loss: 1.3839... Generator Loss: 0.8409
Epoch 1/1... Discriminator Loss: 1.3705... Generator Loss: 0.8178
Epoch 1/1... Discriminator Loss: 1.3845... Generator Loss: 0.8202
Epoch 1/1... Discriminator Loss: 1.3280... Generator Loss: 0.7702
Epoch 1/1... Discriminator Loss: 1.4252... Generator Loss: 0.7671
Epoch 1/1... Discriminator Loss: 1.3832... Generator Loss: 0.8150
Epoch 1/1... Discriminator Loss: 1.3731... Generator Loss: 0.8276
Epoch 1/1... Discriminator Loss: 1.3362... Generator Loss: 0.7579
Epoch 1/1... Discriminator Loss: 1.3329... Generator Loss: 0.9209
Epoch 1/1... Discriminator Loss: 1.3399... Generator Loss: 0.8658
Epoch 1/1... Discriminator Loss: 1.2978... Generator Loss: 0.8634
Epoch 1/1... Discriminator Loss: 1.4944... Generator Loss: 0.8024
Epoch 1/1... Discriminator Loss: 1.4179... Generator Loss: 0.8151
Epoch 1/1... Discriminator Loss: 1.3477... Generator Loss: 0.8783
Epoch 1/1... Discriminator Loss: 1.3729... Generator Loss: 0.6910
Epoch 1/1... Discriminator Loss: 1.4071... Generator Loss: 0.7823
Epoch 1/1... Discriminator Loss: 1.3508... Generator Loss: 0.8262
Epoch 1/1... Discriminator Loss: 1.3422... Generator Loss: 0.8319
Epoch 1/1... Discriminator Loss: 1.3990... Generator Loss: 0.7709
Epoch 1/1... Discriminator Loss: 1.4701... Generator Loss: 0.6678
Epoch 1/1... Discriminator Loss: 1.3560... Generator Loss: 0.9267
Epoch 1/1... Discriminator Loss: 1.2462... Generator Loss: 0.9481
Epoch 1/1... Discriminator Loss: 1.3781... Generator Loss: 0.8324
Epoch 1/1... Discriminator Loss: 1.3745... Generator Loss: 0.7611
Epoch 1/1... Discriminator Loss: 1.2931... Generator Loss: 0.8764
Epoch 1/1... Discriminator Loss: 1.3467... Generator Loss: 0.8461
Epoch 1/1... Discriminator Loss: 1.4033... Generator Loss: 0.7331
Epoch 1/1... Discriminator Loss: 1.3410... Generator Loss: 0.8655
Epoch 1/1... Discriminator Loss: 1.4664... Generator Loss: 0.7318
Epoch 1/1... Discriminator Loss: 1.4138... Generator Loss: 0.7779
Epoch 1/1... Discriminator Loss: 1.4072... Generator Loss: 0.7950
Epoch 1/1... Discriminator Loss: 1.3152... Generator Loss: 0.8841
Epoch 1/1... Discriminator Loss: 1.3679... Generator Loss: 0.8279
Epoch 1/1... Discriminator Loss: 1.4144... Generator Loss: 0.7911
Epoch 1/1... Discriminator Loss: 1.4129... Generator Loss: 0.7684
Epoch 1/1... Discriminator Loss: 1.3731... Generator Loss: 0.7466
Epoch 1/1... Discriminator Loss: 1.3372... Generator Loss: 0.8346
Epoch 1/1... Discriminator Loss: 1.4051... Generator Loss: 0.7726
Epoch 1/1... Discriminator Loss: 1.4214... Generator Loss: 0.7393
Epoch 1/1... Discriminator Loss: 1.3676... Generator Loss: 0.8121
Epoch 1/1... Discriminator Loss: 1.3734... Generator Loss: 0.7593
Epoch 1/1... Discriminator Loss: 1.3798... Generator Loss: 0.7630
Epoch 1/1... Discriminator Loss: 1.4158... Generator Loss: 0.7967
Epoch 1/1... Discriminator Loss: 1.3586... Generator Loss: 0.8437
Epoch 1/1... Discriminator Loss: 1.3717... Generator Loss: 0.8225
Epoch 1/1... Discriminator Loss: 1.4107... Generator Loss: 0.8432
Epoch 1/1... Discriminator Loss: 1.3804... Generator Loss: 0.7350
Epoch 1/1... Discriminator Loss: 1.3343... Generator Loss: 0.8174
Epoch 1/1... Discriminator Loss: 1.4039... Generator Loss: 0.7339
Epoch 1/1... Discriminator Loss: 1.3654... Generator Loss: 0.7920
Epoch 1/1... Discriminator Loss: 1.3287... Generator Loss: 0.8851
Epoch 1/1... Discriminator Loss: 1.3466... Generator Loss: 0.7481
Epoch 1/1... Discriminator Loss: 1.4033... Generator Loss: 0.7390
Epoch 1/1... Discriminator Loss: 1.4038... Generator Loss: 0.7913
Epoch 1/1... Discriminator Loss: 1.3704... Generator Loss: 0.7970
Epoch 1/1... Discriminator Loss: 1.3593... Generator Loss: 0.7836
Epoch 1/1... Discriminator Loss: 1.3411... Generator Loss: 0.8068
Epoch 1/1... Discriminator Loss: 1.3973... Generator Loss: 0.7879
Epoch 1/1... Discriminator Loss: 1.3317... Generator Loss: 0.7823
Epoch 1/1... Discriminator Loss: 1.3826... Generator Loss: 0.7588
Epoch 1/1... Discriminator Loss: 1.4922... Generator Loss: 0.8285
Epoch 1/1... Discriminator Loss: 1.3067... Generator Loss: 0.7368
Epoch 1/1... Discriminator Loss: 1.3561... Generator Loss: 0.8100

提交项目

提交本项目前,确保运行所有 cells 后保存该文件。

保存该文件为 "dlnd_face_generation.ipynb", 并另存为 HTML 格式 "File" -> "Download as"。提交项目时请附带 "helper.py" 和 "problem_unittests.py" 文件。

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